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econometrics: Definition and Much More from Answers.com

  • ️Wed Jul 01 2015

Econometrics literally means 'economical measurement' though the grammatically correct term from Greek would be economometrics (the word has been shortened in English). Econometricians are concerned with the tasks of developing and applying quantitative or statistical methods to the study and elucidation of Economics principles.[1] Econometrics is derived from mathematical economics, statistics, statistical economics, and economics theory.

Purpose

The two main purposes of econometrics are to give empirical content to economic theory and to subject economic theory to potentially falsifying tests.[2] For example, economic theory hypothesize that a person with more education will on average earn more income than person with less education holding everything else equal. Econometric estimates can estimate the magnitude and statistical significance of the relation.

Methods

The most important statistical method in econometrics is regression analysis. For an overview of a linear implementation of this framework, see linear regression. Regression methods are important in econometrics because economists typically cannot use controlled experiments. Observational data may be subject to omitted-variable bias and other problems which must be addressed statistically using regression models. Econometricians often seek illuminating natural experiments in the absence of evidence from controlled experiments.

Econometric analysis is divided into time-series analysis and cross-sectional analysis. Time-series analysis examines variables over time, such as the effects of population growth on a nation's GDP. Cross-sectional analysis examines the relationship between different variables at a point in time; for instance, the relationship between individuals' income and food expenditures. When time-series analysis and cross-sectional analysis are conducted simultaneously on the same sample, it is called panel analysis. If the sample is different each time, it is called repeated cross section data. Multi-dimensional panel data analysis is conducted on data sets that have more than two dimensions. For example, some forecast data sets provide forecasts for multiple target periods, conducted by multiple forecasters, and made at multiple horizons. The three dimensions provide more information than can be gleaned from two dimensional panel data sets.

Econometric analysis may also be classified on the basis of the number of relationships modelled. Single equation methods model a single variable (the dependent variable) as a function of one or more explanatory variables. In many econometric contexts such single equation methods may not be able to recover estimates of causal relationships because either the dependent variable causes changes in one of the explanatory variables or because variables not included in the model cause both the dependent and at least one of the independent variables. Simultaneous equation methods have been developed as one means of addressing these problems. Many of these methods use variants of instrumental variables models to make estimates.

Much larger econometric models are used in an attempt to explain or predict the behavior of national economies.

Example

A simple example of a relationship in econometrics applicable to labor economics is:

ln(wage) = constant + (rate of return to education) * (education) + ε

In this equation, the natural logarithm of a person's wage is a linear function of the number of years of education he has. The econometric goal is to estimate the expected percentage change in wages a person would receive if she obtained one more year of education. є here denotes the standard error.

If the researcher could randomly assign people to differing levels of education, the correlation between education and wages would reveal the causal effect of education on wages. But it is not feasible to conduct such experiments. Instead the econometricians only observes how many years of education people obtain, and the wages they receive. The correlation between wages and education reflects both the effect of education on wages and unobserved variables which may affect both outcomes. For example, more intelligent people may tend to obtain more education and may also earn more at any level of education than less intelligent people. Thus the sampled rate of return would measure both the market rate of return and higher wages due to innate intelligence not related to education. The model attempts to uncover only the rate of return to education, so the effect from innate intelligence must be taken out. One way to accomplish this is through Instrumental Variables. Other econometric methods could also be used to overcome these problems and estimate the underlying causal effect of education on wages.[3]

Notable Econometricians

Nobel Memorial Prize in Economics recipients in the field of econometrics:

The Econometric Author Links of the Econometrics Journal provides personal links to recent articles and working papers of econometric authors via the RePEc system in EconPapers

Software

Software packages that are widely used by professional statisticians include B34S, SAS, OxMetrics, PcGive, Stata, RATS, TSP, SPSS, and WinBugs. For more details see references indicated below.

See also

References

  1. ^ Ragnar Frisch (1933). "Editor's Note". Econometrica 1. 1-4.
  2. ^ * M. Pesaren Hashem. "econometrics,"The New Palgrave: A Dictionary of Economics, v. 1 (1987), pp. 8-22.
  3. ^ David Card, "The Causal Effect of Education on Earnings," in Orley Ashenfelter and David Card, ed., Handbook of Labor Economics, v. 3. Amsterdam: Elsevier (1999).

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